The representations of atmospheric moist convection in general circulation models have been one of the most challenging tasks due to its complexity in physical processes, and the interaction between processes under different time/spatial scales. This study proposes a new method to predict the effects of moist convection on the environment using convolutional neural networks. With the help of considering the gradient of physical fields between adjacent grids in the grey zone resolution, the effects of moist convection predicted by the convolutional neural networks are more realistic compared to the effects predicted by other machine learning models. The result also suggests that the method proposed in this study has the potential to replace the conventional cumulus parameterization in the general circulation models.
翻译:由于大气在物理过程中的复杂性,以及不同时间/空间尺度下各过程之间的互动,大气湿对流模型在一般环流模型中的表现一直是最具挑战性的任务之一。本研究报告提出了一种新的方法,用进化神经网络预测湿对流对环境的影响。通过帮助考虑灰色区域分辨率相邻网格之间的物理场梯度,同其它机器学习模型预测的影响相比,进化神经网络预测的潮流对流的影响更为现实。结果还表明,本研究报告中建议的方法有可能取代一般环流模型中常规累积参数化。